Human activity recognition using earable device
Tahera Hossain, Md Shafiqul Islam, Md Atiqur Rahman Ahad, Sozo Inoue · 2019
Wearable sensors are monumental for human activity recognition. Researchers are continuously inventing new technology to detect human activity properly. Earable opens up interesting possibilities of monitoring personal scale behavioral activities. In this paper, we explore earables device 'eSense' multisensory stereo device for personal scale behavior analysis. We propose an activity recognition framework by exploiting eSense based multi-sensory device. It has a microphone, 6-axis inertial measurement unit, and a dual-mode Bluetooth. We use eSense accelerometer sensor data for detecting head and mouth related behavioral activities. We develop a data collection framework from the eSense through our smartphone application via Bluetooth. Then from the collected data, a few statistical features are computed to classify six personal scale activities related to head and neck movement such as speaking, eating, headshaking and head nodding, as well as, stay and walk. We aggregate the time series data intzo different action labels that summarize the user activity over a time interval. After, we train the data to induce a predictive model for activity recognition. We explore both machine learning and deep learning approach for data classification. For classification, we use the Support Vector Machine, Random Forest, and K-Nearest Neighbor and Convolutional Neural Network and achieve satisfactory recognition accuracy. The findings provide promising prospect for eSense for personal scale activity recognition in healthcare monitoring service. Based on our study, this kid of work is done for the first time with satisfactory findings.